Stretchable and self-adhesive triboelectric sensor for real-time musculoskeletal monitoring and personalized recovery

Robot-assisted gait training has been reported to improve gait in individuals with hemiparetic stroke. Ideally, the gait training program should be customized based on individuals' gait characteristics and longitudinal changes. However, a gait robot that uses gait characteristics to provide individually tailored gait training has not been proposed. The new gait training robot, "Welwalk WW-2000," permits modification of various parameters, such as time and load of mechanical assistance for a patient's paralyzed leg. The robot is equipped with sensors and a markerless motion capture system to detect abnormal hemiparetic gait patterns during robot-assisted gait training. Thus, it can provide individually tailored gait training. This study aimed to investigate the criterion validity of the gait analysis system in the Welwalk WW-2000 in healthy adults. Twelve healthy participants simulated nine abnormal gait patterns that were often manifested in individuals with hemiparetic stroke while wearing the robot. Each participant was instructed to perform a total of 36 gait trials, with four levels of severity for each abnormal gait pattern. Fifteen strides for each gait trial were recorded using the markerless motion capture system in the Welwalk WW-2000 and a marker-based three-dimensional (3D) motion analysis system. The abnormal gait pattern index was then calculated for each stride from both systems. The correlation of the index values between the two methods was evaluated using Spearman's rank correlation coefficients for each gait pattern in each participant. Using the participants' index values for each abnormal gait pattern obtained using the two motion analysis methods, the median Spearman's rank correlation coefficients ranged from 0.68 to 0.93, which corresponded to moderate to very high correlation. The gait analysis system in the Welwalk WW-2000 for real-time detection of abnormal gait patterns during robot-assisted gait training was suggested to be a valid method for assessing gait characteristics in individuals with hemiparetic stroke. [https://jrct.niph.go.jp], identifier [jRCT 042190109].

ABSTRACTBackground: Most people with hemiplegia experience gait changes after a stroke. Abnormal gait patterns in stroke patients vary across subjects and this make it difficult to assess the cause of gait abnormalities. Therefore, it is necessary to quantitatively evaluate abnormal gait patterns through gait analysis for stroke patients.Objective: To develop and evaluate the validity of quantitative assessments of the degree of knee extensor thrust, flexed-knee gait, insufficient knee flexion during the swing phase, and medial whip.Methods: Forty-six healthy control subjects and 112 people with hemiplegia participated. From the 112 patients, 50 patients were selected into each abnormal gait pattern (knee extensor thrust, flexed-knee gait, insufficient knee flexion during the swing phase, and medial whip) with some overlap. Participants were instructed to walk on a treadmill and were recorded using a three-dimensional motion analysis system. An index to quantify each of the four abnormal gait patterns exhibited by the patients was calculated from the three-dimensional coordinate data. The indices were developed based on the definition of the abnormal gait patterns. The index values for the patients were compared with those of healthy subjects as well as with the results of observational gait assessment by three physical therapists with expertise in gait analysis.Results: Strong correlation was observed between the index value and the median observational rating for all four abnormal gait patterns (−0.64 to −0.86). Most of the patients with an abnormal gait pattern had a higher index value than the healthy subjects.Conclusions: The use of these indices in gait analysis of people with hemiplegia can help to diagnose severity of gait disorder, determine the appropriate treatment, and evaluate the effectiveness of the treatment.

Background: Gait assessment is important to determine the most effective strategy to regain gait function during stroke rehabilitation. To understand the mechanisms that cause abnormal gait patterns, it is useful to objectively identify and quantify the abnormal gait patterns. Objective assessment also helps evaluate the efficacy of treatments and can be used to provide suggestions for treatment.Objective: To evaluate the validity of quantitative indices for retropulsion of the hip, excessive hip external rotation, and excessive lateral shift of the trunk over the unaffected side in hemiplegic patients.Methods: Forty-six healthy control subjects and 112 hemiplegic patients participated. From the 112 patients, 50 patients were selected into each abnormal gait pattern with some overlap. Participants were instructed to walk on a treadmill and were recorded using a three-dimensional motion analysis system. An index to quantify each of the three abnormal gait patterns was calculated from the three-dimensional coordinate data. The index values of patients were compared with those of healthy subjects and with the results of observational gait assessment by three physical therapists with expertise in gait analysis.Results: Strong correlation was observed between the index value and the median observational rating for all three abnormal gait patterns (−0.56 to −0.74). Most of the patients with an abnormal gait pattern had a higher index value than the healthy subjects.Conclusions: The proposed indices are useful for clinical gait analysis. Our results encourage a more detailed analysis of hemiplegic gait using a motion analysis system.

This article mainly discusses the application of real-time environmental monitoring technology in educational practices. It begins by introducing the basic principles of this technology, which involves real-time monitoring of environmental parameters through sensors and the use of data analysis and processing for real-time evaluation and feedback. It then delves into the specific applications of real-time environmental monitoring technology in teaching, including classroom instruction and laboratory practices. The article analyzes the advantages of this technology in teaching, such as real-time monitoring, objectivity, and personalization, while also addressing challenges such as sensor reliability and accuracy, teacher training, and strategy improvement. Finally, it provides methods and strategies to address the challenges in the application of real-time environmental monitoring technology, including improving sensor technology, enhancing teacher training, and refining teaching strategies. Overall, real-time environmental monitoring technology has significant potential for application in education, but it also requires addressing various technological and educational challenges.

Background Assessing abnormal gait patterns could indicate compensatory movements, which could be an index for recovery and a process of motor learning. To quantify the degree of posterior pelvic tilt, contralateral vaulting is necessary. Objectives This study aimed to develop and evaluate the validity of quantitative indices for posterior pelvic tilt and contralateral vaulting in hemiplegic patients. Methods Forty-six healthy control subjects and 112 hemiplegic patients participated in this study. Of the 112 patients, 50 were selected into each abnormal gait pattern group, with some overlap. Three experienced physical therapists observed their walking and graded the severity of the two abnormalities in five levels. An index to quantify each of the two abnormal gait patterns was calculated from the three-dimensional treadmill gait analysis. The index values of patients were compared with those of healthy subjects and with the results of observational gait assessment done by three physical therapists with expertise in gait analysis. Results The index values were significantly higher in hemiplegic patients than in healthy subjects (28.0% and 44.7% for the posterior pelvic tilt in healthy subjects and patients, respectively and 0.9 and 4.7 for the contralateral vaulting, respectively). A strong correlation was observed between the index value and the median observational rating for two abnormal gait patterns (r = −0.68 and −0.72). Conclusions The proposed indices for posterior pelvic tilt and contralateral vaulting are useful for clinical gait analysis, and thus encouraging a more detailed analysis of hemiplegic gait using a motion analysis system.

: An unprecedented opportunity exists to introduce real-time physiological and environmental monitoring technology into future US Army dismounted forces for use in both training and combat situations. The motivation is to enhance the survivability of the individual warfighter and to provide increased situational awareness to both combat medics and commanders during the course of a mission or field operation. The monitoring technology must be reliable, must be unobtrusive, and compelling in terms of value to both the lowest-echelon warfighters and their command chain. Realizing these objectives will require adapting and extending ambulatory medical monitoring technology well beyond the capabilities of current commercial devices and systems, and will place the US Army in a unique position with regard to real-time physiological status and health monitoring. This report identifies specific technology and system level issues that must be addressed to realize the objective system and proposes both a near-term and far-term system concept and development strategy. Technology developments critical to success include covert wireless personal area networking, physiological and environmental sensors hardened for the dynamic warfighter environment, and real-time data processing and fusion algorithms to extract the relevant physiological information and overall health status.

The objective of this study was to classify the 10 types of characteristic abnormal gait by principal component analysis using quantitative indices of 10 types of abnormal gait. For abnormal gait pattern classification, principal component analysis was performed using the deviation values of the 10 types of abnormal gait of 90 subjects. Scatter plots of the factor loadings of the 1st and 2nd principal components of the 10 types of abnormal gait were prepared, and those arranged at near sites were grouped based on the positional relationship, through which abnormal gait patterns were classified. It was suggested that abnormal gait patterns can be classified into insufficient knee flexion, hip hiking, and excessive lateral shift of the trunk over the unaffected side in the swing phase; knee extensor thrust pattern accompanying forefoot contact in the stance phase in addition to circumduction gait of the swing phase; and flexed knee gait pattern accompanying retropulsion of the hip in addition to median whip in the stance phase and excessive hip external rotation in the swing phase. It was clarified by these principal component analyses that information contained in the results of the 10 quantitative indices of abnormal gait of the 90 poststroke hemiplegia patients was compressed into several abnormal gait patterns. If observational abnormal gait analysis is performed after understanding this gait pattern classification, it may help improve the accuracy of gait analysis by observation.

IntroductionGait robots have the potential to analyze gait characteristics during gait training using mounted sensors in addition to robotic assistance of the individual’s movements. However, no systems have been proposed to analyze gait performance during robot-assisted gait training. Our newly developed gait robot,” Welwalk WW-2000 (WW-2000)” is equipped with a gait analysis system to analyze abnormal gait patterns during robot-assisted gait training. We previously investigated the validity of the index values for the nine abnormal gait patterns. Here, we proposed new index values for four abnormal gait patterns, which are anterior trunk tilt, excessive trunk shifts over the affected side, excessive knee joint flexion, and swing difficulty; we investigated the criterion validity of the WW-2000 gait analysis system in healthy adults for these new index values.MethodsTwelve healthy participants simulated four abnormal gait patterns manifested in individuals with hemiparetic stroke while wearing the robot. Each participant was instructed to perform 16 gait trials, with four grades of severity for each of the four abnormal gait patterns. Twenty strides were recorded for each gait trial using a gait analysis system in the WW-2000 and video cameras. Abnormal gait patterns were assessed using the two parameters: the index values calculated for each stride from the WW-2000 gait analysis system, and assessor’s severity scores for each stride. The correlation of the index values between the two methods was evaluated using the Spearman rank correlation coefficient for each gait pattern in each participant.ResultsThe median (minimum to maximum) values of Spearman rank correlation coefficient among the 12 participants between the index value calculated using the WW-2000 gait analysis system and the assessor’s severity scores for anterior trunk tilt, excessive trunk shifts over the affected side, excessive knee joint flexion, and swing difficulty were 0.892 (0.749–0.969), 0.859 (0.439–0.923), 0.920 (0.738–0.969), and 0.681 (0.391–0.889), respectively.DiscussionThe WW-2000 gait analysis system captured four new abnormal gait patterns observed in individuals with hemiparetic stroke with high validity, in addition to nine previously validated abnormal gait patterns. Assessing abnormal gait patterns is important as improving them contributes to stroke rehabilitation.Clinical trial registrationhttps://jrct.niph.go.jp, identifier jRCT 042190109.

Integrating Artificial Intelligence (AI) into Unmanned Aerial Vehicle (UAV) operations has advanced efficiency, safety, and decision-making. This study addresses critical gaps in UAV methods, including insufficient integration of human factors, operator variability, and the lack of systematic error analysis. To overcome these challenges, a novel approach combines the Analytic Hierarchy Process (AHP) with three core human factors models: the Observe-Orient-Decide-Act (OODA) loop, the Human Factors Analysis and Classification System (HFACS), and the SHELL model. An online survey was conducted across diverse UAV operator groups to prioritize critical factors within each model. Additionally, real-time monitoring of heart rate (HR), heart rate variability (HRV), and respiratory rate (RR) was conducted during UAV operations at various automation levels with different experience levels. Visualization through boxplots and percentage change matrices provided insights into operator stress and workload across automation levels. Integrating AHP findings and physiological data revealed significant differences in operator prioritization, highlighting the need for tailored AI-UAV strategies. This research combines survey data with real-time physiological monitoring, offering visions into optimizing human-AI interaction in UAV operations and providing a foundation for improving AI integration and operator strategies.

Real-time monitoring technology in single-case experimental design research: Opportunities and challenges

Hip osteoarthritis (OA), or the degeneration of cartilage in the hip joint, is a common and chronic condition that is growing in prevalence around the world. OA typically causes significant joint pain, lack of mobility, and abnormal gait patterns in affected individuals. Total hip arthroplasty (THA) is used to treat OA, and of the many postoperative methods of assessing success of the procedure, one that is particularly useful is gait analysis. Gait analysis provides a quantitative view of patient gait biomechanics by examining many relevant gait parameters and is very useful to evaluate sequelae following THA. The present paper synthesizes the recent literature surrounding post-THA gait analysis to gain a deeper understanding of how gait analysis may be used to improve THA and its corresponding patient outcomes.

Abnormal gait patterns are a common feature of Cerebral Palsy, a neurodevelopmental disease for which early identification is essential for treatment. In the proposed research, a novel methodology is provided for classifying abnormal gait patterns in children with Cerebral Palsy, using gait analysis as a diagnostic tool. To improve gait classification accuracy and efficiency, a hybrid model of Convolutional Long Short-Term Memory (ConvLSTM) model and Generative Adversarial Network (GAN) is used in the suggested technique. The proposed study concentrated on temporal signal data, using hypothetical planes with minimal regard for anatomical indicators. The reduction technique enables a more efficient and successful gait analysis. Heatmap images were created from the selected temporal data. GAN generated images were added to the dataset in order to overcome the problems caused by class imbalance and guarantee a thorough depiction of abnormal gait patterns. In the proposed work, a ConvLSTM-based model with a batch size of 32, training as well as validation datasets were evaluated over a period of 50 epochs. The effectiveness of the suggested model was compared to other models such as Gated Recurrent Unit, Convolutional Neural Network, and Long Short-Term Memory model that were trained using the same input data. Our suggested ConvLSTM model produced an impressive accuracy of 91.8% and a loss of 0.42. The Convolutional Long Short Term Memory model performed better than the other models when compared based on a number of criteria, including accuracy, precision, recall, and F1-score. The performance measures demonstrate how well our method works to classify the abnormal gait in kids with Cerebral Palsy.

A sampling inspection using non-patterned wafer and photomask has been dedicated to classical inspection technology for monitoring trend of particle variation at mass production. Total cost of sampling inspection method decreases the overall equipment effectiveness in mass production because equipment usage time and raw material cost. Nevertheless, customer mass production yield requirements for high-grade photomask and extreme ultraviolet photomask by sampling inspection method will be difficult to satisfy. To overcome sampling inspection's low reliability, this paper intended to describe an application of real-time monitoring for mass production equipment and verification of evaluated case by case. Optimization of real-time monitoring setup requires that sensor's install location with a considered mean free path in vacuum chamber, avoid to particle and bubble in chemical tube line and filter, and digital image process comparing method for nozzle height and parts location. An emergence of many by-products in a vacuum chamber, chemical tube line, and chemical filter is an unexpected danger. Application of real-time monitoring contributes to observing particles that in vacuum chamber using plasma, in tube line using chemical and chemical filters, sensing of mechanical drift and twist are also applicable with real-time detection technology using high-resolution cameras. As mentioned above, saved real-time big data can use proactive control to improve yield loss and cost of ownership. The specific suggestion about using real-time monitoring method is as follows 1. Detected increase rapidly trend of particle. Stop a process and start a particle removing recipe. 2. Observed particle rising. Stop process and start a cure recipe, and change other best path. 3. Sensed abnormal action. Stop process and do preventive maintenance after all substrate out. Both real-time monitoring data and yield data can analyze correlations that improve to become low cost of ownership by figuring out a root cause and drop in quality. A photomask industry is small compare to semiconductor industries, less than 1 percent by number of tools and production capacity. A photomask industry hard to make a big data due to little seed or small volume data. This paper shows how to make big data using real-time monitoring technology and how to defend a yield loss by unexpected situation at photomask tools.

Under the guidance of the "double carbon" goal, China energy system is undergoing profound changes. The development of new energy storage is an important way to improve the flexibility of China power system, build a new power system and ensure the realization of the "double carbon" goal on schedule. The "Virtual Power Plant" technology can realize the aggregation and optimization of energy storage devices, realize the multi energy complementarity on the power side, promote the flexible interaction on the load side, and provide a new operation scheme for building a security, economic, efficient and reliable power grid. However, at the same time, the massive distributed loads typical of the new energy storage are distributed everywhere. How to realize the real-time, security and reliable monitoring and control has become an important problem affecting the wide application of the new energy storage technology. Focusing on the real-time, security and reliable monitoring and control of the distributed energy storage loads, this paper proposes a real-time monitoring and control technology for distributed energy storage based on 5G to ensure the security and reduce the time delay of application data interaction. It provides a feasible and effective solution for the main station of power system to realize real-time load control and meet the needs of peak cutting and valley filling.

Welwalk WW-1000 is a gait exercise robotic assist system that allows subjects to walk on treadmill by attaching a knee-ankle-foot robot to a paralyzed limb. Abnormal gait patterns during exercise using Welwalk WW-1000 are evaluated by gait observation or marker-based motion analysis systems. However, gait observation is a subjective and ordinal measure, and marker-based motion analysis systems are challenging to implement due to the complexity of preparing equipment and attaching markers to subjects. In this study, we propose the Welwalk WW-2000 system, which incorporated a marker-less motion analysis system that detects abnormal gait patterns during exercise using the robotic system. Using this system, it is expected that a gait exercise program can be planned from easily obtainable, objective information. This system detects the features of abnormal gait patterns using the body position coordinates of the subject obtained from three-dimensional, inertial, knee angle, and load sensors. The purpose of this study was to validate the marker-less motion analysis system against marker-based motion analysis systems. One healthy male simulated the seven abnormal gait patterns which occur frequently in stroke patients, with four grades of severity. Spearman"s rank correlation coefficients were calculated for the relationship between the abnormal gait pattern parameters calculated by each motion analysis system. The correlations between the two systems ranged from 0.81 to 0.95. Therefore, it was confirmed that the marker-less motion analysis system of the Welwalk WW-2000 was valid.